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Mathematical Sciences Colloquium by Kyunghee Han
Tuesday, Jan. 14
3 p.m. - 4 p.m. Location: FO 2.404

Kyunghee Han

University of Pennsylvania

Additive Functional Regression for Densities as Responses

Distributions or densities are commonly encountered as functional outcomes under shape constraints, which are monotone or non-negative. Examples include age distributions for calendar years, survival functions for different countries, and gene expression curves from microarray experiments. In such applications, functional regression models associating covariates with distributions or densities are modeling problems that incorporate geometry, inherent to the space of distribution or density functions, into the regression analysis. In this talk, I will present the nonparametric estimation of additive functional regression models for distributions or densities as responses, given multiple covariates. Additive modeling is attractive and widely applicable for practical situations where dimensions of covariates are large since the estimation of unstructured models is subject to the curse of dimensionality. Extending the Fréchet mean with the Wasserstein metric, I will introduce a smooth back-fitting algorithm for estimating the proposed additive functional regression models and establish the consistency of the back-fitting estimator, including the rate of convergence.

Coffee to be served 30 minutes prior to the talk in the alcove outside of FO 2.406.

 

Persons with disabilities may submit a request for accommodations to participate in this event at UT Dallas' ADA website. You may also call (972) 883-5331 for assistance or send an email to [email protected]. All requests should be received no later than 2 business days prior to the event.
Contact Info:
Viswanath Ramakrishna, 972-883-6873
Questions? Email me.

Tagged as Lectures/Seminars, Professional Dev.
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